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Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data
In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions.
Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
Drug discovery is a complex process that involves sequentially screening and examining a vast array of molecules to identify those with the target properties.
Language Model Prompt Selection via Simulation Optimization
We propose sequentially selecting the prompt for evaluation based on this constructed surrogate model.
Causal inference with Machine Learning-Based Covariate Representation
Utilizing covariate information has been a powerful approach to improve the efficiency and accuracy for causal inference, which support massive amount of randomized experiments run on data-driven enterprises.
